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Climate Downscaling For Hazardous Plume Transport

Projects

Overview

Probability that the surface dosage will exceed a user–defined threshold, for a simulated agent released over Gaza City 6 h prior to the time shown here. To obtain this estimate, ClimoFDDA produced the "typical" atmospheric conditions and uncertainties during the nighttime in September from a 20–year downscaled re–analysis.

Our motivation for this work is twofold. First, emergency managers must develop plans of action for responding to releases of hazardous material into the atmosphere, especially for high–profile events where many people gather into a small area (e.g., the Olympic Games). Second, when military commanders prepare their strategic battle plans, they must distill a vast array of information about the area where operations will take place, to identify hazards that airborne and ground forces will face on the battlefield. In both cases, a key piece of information is the typical "weather" one might encounter, and how this may help or hinder operations. This kind of planning takes place weeks or months in advance of the actual operations, well beyond the range where we can explicitly forecast individual weather features (or events) with any credibility. One alternative is to use climatographical data to help define the "typical" atmospheric conditions for a given day. Climatographic information derived from the global re–analysis such as the NCEP/NCAR Reanalysis and the European Center for Medium Range Weather Forecasting 40–year Reanalysis have limited utility because they are done on a very coarse grid (∼100–250 km) which does not define local–scale circulations in and around complex topography and coastal margins. These circulations strongly influence both the sensible weather and the climate, as well as the transport and dispersion (T&D) processes within the boundary layer.

NCAR's RAL is engaged in an array of National and international projects to create high–resolution downscaled reanalyses for T&D applications using NCAR's state–of–the–art Climate Four Dimensional Data Assimilation (ClimoFDDA) system that is based upon the Weather Research and Forecasting (WRF) model. The common goal for all these projects is to produce highly refined estimates of the "typical" atmospheric conditions for any given hour, day, month or season, significantly enhancing the risk assessment and strategic planning capabilities.

Real-Time Four Dimensional Data Assimilation (RT-FDDA)

The RTFDDA system is the product of eight years of research and development sponsored largely by the U.S. Army Test and Evaluation Command (ATEC). At the core of the system is NCAR's Weather Research and Forecasting Model (WRF).

The RTFDDA system has two main components. The first component employs the WRF Model model in four-dimensional data assimilation (FDDA) mode, wherein artificial tendency terms are used in the prognostic equations to continuously steer the model toward the true state of the atmosphere. This FDDA system runs continuously, assimilating surface mesonet data, radiosonde data, satellite-derived cloud-track winds, surface-based profiler data, and Automated Commercial Aircraft Reporting System data, and NASA QuikSCAT SeaWinds. We are also exploring the use of NASA Earth Science datasets acquired in real-time through the MODIS Aqua and Terra sensors. Specifically, we are investigating the use of Level 3 composite sea surface temperature (SST) data, composite snow cover data, leaf area index (LAI), and fraction of photosynthetically active radiation (FPar).

The second component of the system uses the WRF Model in a purely forecast mode, where the forecasts are initiated from the model-assimilated data sets at an interval of 3 h, with a forecast duration of 36 h. The version of the WRF Model used for this work is non-hydrostatic and uses telescoping, two-way-interacting, computational grids, with the finest grid centered over the the New York City area.

Physical process parameterizations include: the next-generation Medium Range Forecast (MRF) model PBL scheme developed by Yonsei University (YSU), Grell and Devenyi (2002) ensemble cumulus parameterization on grids using a 10-km or larger grid increment, a modified version of the Oregon State University land-surface model (Chen and Dudhia 2001 a,b), the explicit cloud microphysical scheme of Lin et al. (1983) and Rutledge and Hobbs (1984), which predicts the mixing ratio of five hydrometeor species (cloud droplets, cloud ice, rain, snow, and graupel), and shortwave and longwave radiation effects are represented by the Dudhia (1989) and Mlawer et al. (1997) schemes, respectively.

Weather Researach and Forecasting Model (WRF)

The WRF Model is a next-generation mesocale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. WRF is suitable for a broad spectrum of spatial scales ranging from meters to thousands of kilometers.

The WRF Model has been developed through a collaboration between the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (the National Centers for Environmental Prediction (NCEP) and the Earth System Research Laboratory (ESRL) Global Systems Division (GSD), the U.S. Air Force Weather Agency (AFWA), the Naval Research Laboratory, Oklahoma University, and the Federal Aviation Administration (FAA).